Rapid IO routing strategy based on improved glowworm swarm optimization algorithm

Congyue YIN; Xingming ZHANG; Quan REN; Shuai WEI

doi:10.11959/j.issn.2096-109x.2018054

您当前的位置：

首页 >

文章列表页 >

Rapid IO routing strategy based on improved glowworm swarm optimization algorithm

Papers | 更新时间：2024-06-03

- Rapid IO routing strategy based on improved glowworm swarm optimization algorithm
- Chinese Journal of Network and Information Security Vol. 4, Issue 6, Pages: 52-61(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018054
  CLC： TP393.02
- Online First：2018-06，
  
  Published：15 June 2018
- 稿件说明：
移动端阅览
Congyue YIN, Xingming ZHANG, Quan REN, et al. Rapid IO routing strategy based on improved glowworm swarm optimization algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 52-61.
DOI：

Congyue YIN, Xingming ZHANG, Quan REN, et al. Rapid IO routing strategy based on improved glowworm swarm optimization algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 52-61. DOI： 10.11959/j.issn.2096-109x.2018054.

摘要

针对Rapid IO网络Qo S路由选择问题，提出一种基于改进萤火虫算法的Rapid IO路由选择策略。首先，利用高斯变异和存储机制对传统萤火虫算法进行优化，高斯变异可以有效控制算法搜索空间中解的散射程度，使算法避免陷入局部最优，存储机制有利于评估并存储每只萤火虫的历史状态，防止信息丢失。然后，将改进后的萤火虫算法与实际Rapid IO网络Qo S问题相结合，选择出最终的最佳路由策略。实验结果表明，在所模拟的Rapid IO测试网络中，改进后的萤火虫算法时延为42 ms，时延抖动为8 ms，代价最低为64 ms，共需要迭代的次数为8，相较于其他算法曲线更加稳定，更能快速找到最优解，表现出的性能最优，有效解决了Rapid IO网络Qo S路由选择问题。

Abstract

Aiming at the problem of Qo S routing in Rapid IO network

a Rapid IO routing strategy based on improved glowworm swarm optimization algorithm was proposed.Firstly

gaussian mutation and storage mechanism were used to optimize the traditional firefly algorithm.Gaussian mutation can effectively control the scattering degree of the solution in the search space of the algorithm

so that the algorithm avoids falling into a local optimum.The storage mechanism is conducive to evaluating and storing the historical state of each glowworm

preventing information loss.Then combine the improved glowworm swarm optimization algorithm with the actual Rapid IO network Qo S problem and select the final best routing strategy.The experimental results show that in the simulated Rapid IO test network

the improved glowworm swarm optimization algorithm has a delay of 42 ms

delayed jitter of 8 ms

a minimum cost of 64 ms

and a total of 8 iterations

which is more stable than other algorithm curves.It can find the optimal solution more quickly and show the best performance

effectively solving the Qo S routing problem of Rapid IO network.

关键词

Keywords

references

张娟 , 苏海冰 , 吴钦章 . 基于多处理器的高速 RapidIO [J ] . 计算机工程 , 2010 , 36 ( 18 ): 238 - 239 .

ZHANG J , SU H B , WU Q Z . Multi processor based on high speed RapidIO [J ] . Computer Engineering , 2010 , 36 ( 18 ): 238 - 239 .

黄亮 , 刘福岩 . 基于RapidIO和存储映射的高速互连网络 [J ] . 计算机工程 , 2008 , 34 ( 14 ): 116 - 117 .

HUANG L , LIU F Y . High speed Internet based on RapidIO and storage mapping [J ] . Computer Engineering , 2008 , 34 ( 14 ): 116 - 117 .

DONG W , ZHANG W , YANG W . Node constraint routing algorithm based on reinforcement learning [C ] // IEEE International Conference on Signal Processing . 2017 : 1752 - 1756 .

QIN L , CHEN Y , LUO J , et al . A new way of solving multiple constrained Qo S multi-routing problems [C ] // International MultiSymposiums on Computer and Computational Sciences . 2006 : 668 - 675 .

潘灵 , 桑楠 . 一种 RapidIO 网络路径分配策略 [J ] . 计算机应用 , 2008 , 28 ( s2 ): 294 - 295 .

PAN L , SANG N . A RapidIO network path allocation strategy [J ] . Computer Application , 2008 , 28 ( s2 ): 294 - 295 .

蔡炜 , 张建东 , 蔡惠智 . RapidIO网络QoS多目标优化 [J ] . 计算机应用 , 2010 , 30 ( 12 ): 3172 - 3175 .

CAI W , ZHANG J D , CAI H Z . RapidIO network QoS multi-objective optimization [J ] . Computer Application , 2010 , 30 ( 12 ): 3172 - 3175 .

蔡炜 , 张建东 , 蔡惠智 . RapidIO 网络路由分配策略的优化和改进 [J ] . 计算机工程与应用 , 2011 , 47 ( 14 ): 87 - 90 .

CAI W , ZHANG J D , CAI H Z . RapidIO network route allocation strategy optimization and improvement [J ] . Computer Engineering and Applications , 2011 , 47 ( 14 ): 87 - 90 .

李宗灿 , 曹建 . 基于约束分析的RapidIO路由选择算法 [J ] . 计算机工程与设计 , 2014 , 35 ( 11 ): 3771 - 3775 .

LI Z C , CAO J . RapidIO routing algorithm based on constraint analysis [J ] . Computer Engineering and Design , 2014 , 35 ( 11 ): 3771 - 3775 .

DONG W , ZHANG W , YANG W . Node constraint routing algorithm based on reinforcement learning [C ] // IEEE International Conference on Signal Processing . 2017 : 1752 - 1756 .

MATVEEVA N , SUVOROVA E , SHEYNIN Y . Qo S mechanisms in Space Fibre and Rapid IO:Space Wire networks and protocols,long paper [C ] // Spacewire Conference . 2016 : 1 - 8 .

JAYASHEELAN P , JANE F M M . Effective route planning in road networks using multi constraint routing algorithm [C ] // IEEE International Conference on Current Trends in Advanced Computing . 2016 : 1 - 6 .

MCKENNY M , DINES J A B , HARLE D . Transporting multiple classes of traffic over a generic routing device - an investigation into the performance of the Rapid IO™ interconnect architecture [C ] // The,IEEE International Conference on Networks . 2003 : 39 - 44 .

ZHANG X , GAO M , LIU G . A Scalable heterogeneous multi-processor signal processing system based on the Rapid IO Interconnect [C ] // International Symposium on Intelligent Information Technology Application Workshops . 2008 : 761 - 764 .

石海洋 . 一种RapidIO交换网络配置方法的设计与实现 [J ] . 航空计算技术 , 2012 , 42 ( 2 ): 132 - 134 .

SHI H Y . Design and implementation of a RapidIO switching network configuration method [J ] . Aviation Computing Technology , 2012 , 42 ( 2 ): 132 - 134 .

王婷 , 戴小氐 , 朱守园 , 等 . 一种静态配置复杂RapidIO网络的方法 [J ] . 信息通信 , 2017 ( 1 ): 136 - 137 .

WANG T , DAI X D , ZHU S Y , et al . A method for statically configuring a complex RapidIO network [J ] . Information and Communication , 2017 ( 1 ): 136 - 137 .

施春辉 , 柴小丽 , 宋慰军 , 等 . 基于SoPC的前端RapidIO接口设计 [J ] . 计算机工程 , 2011 , 37 ( 20 ): 239 - 241 .

SHI C H , CHAI X L , SONG W J , et al . SoPC-based front-end RapidIO interface design [J ] . Computer Engineering , 2011 , 37 ( 20 ): 239 - 241 .

DOLGOPOLIK M V . Smooth exact penalty functions:a general approach [J ] . Optimization Letters , 2018 , 10 ( 3 ): 635 - 648 .

SHAHINZADEH H , MOAZZAMI M , FADAEI D , et al . Glowworm swarm optimization algorithm for solving non-smooth and non-convex economic load dispatch problems [C ] // Iranian Joint Congress on Fuzzy and Intelligent Systems . 2017 : 103 - 109 .

NUNES H , POMBO J , FERMEIRO J , et al . Glowworm swarm optimization for photovoltaic model identification [C ] // Young Engineers Forum . 2017 : 59 - 64 .

SUN J , FANG W , WU X , et al . Qo S multicast routing using a quantum-behaved particle swarm optimization algorithm [J ] . Engineering Applications of Artificial Intelligence , 2011 , 24 ( 1 ): 123 - 131 .

CALSINA A , CUADRADO S , DESVILLETTES L , et al . Asymptotic profile in selection-mutation equations:gauss versus cauchy distributions [J ] . Journal of Mathematical Analysis ＆ Applications , 2016 , 444 ( 2 ): 1515 - 1541 .

WAN C , WANG J , YANG G , et al . Particle swarm optimization based on Gaussian mutation and its application to wind farm micro-siting [C ] // Decision and Control . 2010 : 2227 - 2232 .

LAIDANI N , BARTALI R , GOTTARDI G , et al . Optical absorption parameters of amorphous carbon films from Forouhi Bloomer and Tauc Lorentz models:a comparative study [J ] . Journal of Physics Condensed Matter , 2008 , 20 ( 1 ): 105 - 112 .

任雪倩 , 李金城 , 赵建中 , 等 . 基于串行 RapidIO的 Buffer 层设计 [J ] . 微电子学与计算机 , 2016 , 33 ( 9 ): 47 - 50 .

REN X Q , LI J C , ZHAO J Z , et al . Design of Buffer layer based on serial RapidIO [J ] . Microelectronics and Computers , 2016 , 33 ( 9 ): 47 - 50 .

Views

1308

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰